Aggregate blood pressure responses to serial dietary sodium and potassium intervention: defining responses using independent component analysis.

BACKGROUND: Hypertension is a complex trait that often co-occurs with other conditions such as obesity and is affected by genetic and environmental factors. Aggregate indices such as principal components among these variables and their responses to environmental interventions may represent novel information that is potentially useful for genetic studies. RESULTS: In this study of families participating in the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) Study, blood pressure (BP) responses to dietary sodium interventions are explored. Independent component analysis (ICA) was applied to 20 variables indexing obesity and BP measured at baseline and during low sodium, high sodium and high sodium plus potassium dietary intervention periods. A "heat map" protocol that classifies subjects based on risk for hypertension is used to interpret the extracted components. ICA and heat map suggest four components best describe the data: (1) systolic hypertension, (2) general hypertension, (3) response to sodium intervention and (4) obesity. The largest heritabilities are for the systolic (64%) and general hypertension (56%) components. There is a pattern of higher heritability for the component response to intervention (40-42%) as compared to those for the traditional intervention responses computed as delta scores (24%-40%). CONCLUSIONS: In summary, the present study provides intermediate phenotypes that are heritable. Using these derived components may prove useful in gene discovery applications.

Designing an optimum genetic association study using dense SNP markers and family-based sample.

Genetic association analysis using thousands of single nucleotide polymorphism (SNP) markers has become a promising alternative to genome-wide linkage scan. Analysis based on linkage-disequilibrium (LD) is more efficient because meiotic information of past generations is utilized. However, in addition to the physical distance between the disease locus and a marker locus, numerous other factors such as admixture, genetic drift, and multiple mutations can affect the observed value of LD. The effect of these factors in a genomic LD association study must be carefully analyzed to obtain an efficient study design. In the following review, we consider studies using family-based data and carefully study the effects of some of these important design factors, including the sample size, frequency of SNP markers, and marker density. For example, we conclude that (1) for reasonably frequent SNP markers, a moderately large sample of 500 families is appropriate for a moderately stringent significance level (alpha = 0.00009); (2) to maintain a power of 80%, maximal difference in allele frequencies between the disease gene and a SNP marker varies between 0.1 (under additive model) and 0.5 (multiplicative); (3) a map density of 10 cM is appropriate only under idea scenario (moderately large sample size, equal trait/marker allele frequencies, maximum LD strength etc.). Results shown here should have practical implications to designing efficient LD association studies using dense SNP markers.

Genetic effect on blood pressure is modulated by age: the Hypertension Genetic Epidemiology Network Study.

Genome-wide linkage analysis was performed for systolic and diastolic blood pressures in the Hypertension Genetic Epidemiology Network. We investigated the role of gene-age interactions using a recently developed variance components method that incorporates age variation in genetic effects. Substantially improved linkage evidence, in terms of both the number of linkage peaks and their significance levels, was observed. Twenty-six linkage peaks were identified with maximum logarithm of odds scores ranging between 3.0 and 4.6, 15 of which were cross-validated by the literature. The chromosomal region 1p36 that showed the highest logarithm of odds score in our study was found to be supported by evidence from 3 studies. The new method also led to vastly improved validation across ethnic groups. Ten of the 15 supported linkage peaks were cross-validated between 2 different ethnic groups, and 2 peaks on chromosomal region 1q31 and 16p11 were validated in 3 ethnic groups. In conclusion, this investigation demonstrates that genetic effects on blood pressure vary by age. The improved genetic linkage results presented here should help to identify the specific genetic variants that explain the observed results.

Role of gene expression microarray analysis in finding complex disease genes.

The promise of gene expression studies using microarray technology has inspired much new hope for finding complex diseases genes. It has become clear that complex diseases result from collective actions of many genetic and nongenetic factors. Therefore, genetic dissection of complex diseases should be carried out in a global context. The technology of gene expression microarray analysis (GEMA) can provide such global information on transcription activities of essentially all genes simultaneously. It is hoped that this promising technology can be applied to samples drawn from large-scale, well-defined genetic epidemiological studies and help us untangle the web of pathways leading to complex diseases. However, extremely noisy GEMA data pose serious challenges in terms of the statistical methodologies needed. Extensive work is needed in order to respond to the challenges before one can fully utilize the potential power provided by GEMA. We begin in this paper by identifying several statistical problems related to the application of GEMA to genetic epidemiological analysis, and consider study designs that might benefit from this promising new technology. While it is still too early to tell how much of the enormous potential of GEMA will be realized ultimately, its success will probably depend most critically on the ability of statistical genetics to rise to the challenge of mining information from a sea of noise.